PREP

https://github.com/dlab-geo/r-geospatial-workshop

Click Clone or Download and download the zip file
Upzip the zip file and make a note of the folder in which it is located

Open RStudio and start a new script
Follow along by opening r-geospatial-workshop-pt1-sp2018.html
Install required libraries

install.packages(
  c("sp","rgdal","tmap","classInt","RColorBrewer",
    "ggplot2","leaflet", "ggmap"), dependencies=TRUE
)

Geospatial Data in R

Workshop Goals

Intro to geospatial data
Intro to coordinate reference systems (CRS)
Classes & methods for working with spatial data in R
Mapping geospatial data
Practice

About Me

Patty Frontiera

dlab.berkeley.edu

About you

Who are you?

Why are you here?

Getting Started

Get the workshop files

https://github.com/dlab-geo/r-geospatial-workshop

Click Clone or Download and download the zip file
Upzip the zip file and make a note of the folder in which it is located

Getting Started

Open RStudio and start a new script
Follow along by opening r-geospatial-workshop-pt1-sp2018.html

Getting Started

In RStudio, install required libraries

install.packages(
  c("sp","rgdal","tmap","classInt","RColorBrewer",
    "ggplot2","leaflet", "ggmap"), dependencies=TRUE
)

Geographic Data

are data about locations on or near the surface of the Earth.

Place names

convey geographic information but don’t unambigously specify location

Geospatial data

represent location geometrically with coordinates

46.130479, -117.134167

Coordinate Reference System

Coordinates indicate specific locations on the Earth when associated with a geographic coordinate reference system or CRS.

Geographic Coordinate Reference Systems

specify

the shape of the Earth as the major & minor axes of an ellipsoid
the origin (0,0) - equator (~X), prime meridian (~Y)
fit of the CRS to the Earth (center of the earth), aka Datum
units, eg lat/lon expressed as decimal degrees or DMS

Because of variations in 1-3, there are many geographic CRSs!

WGS84

The World Geodetic System of 1984 is the default geographic CRS used today.

WGS84 is the default CRS for most GIS software

Almost all data with lon/lat coordinates are assumed to be WGS84 unless otherwise specified

[NAD83] is another common geographic CRS used by US agencies like the Census.

WGS84 and NAD83 are so similar that differences are often ignored except for applications requiring high locational accuracy.

Map Projections

A Projected CRS applies a map projection to a Geographic CRS

Map Projection: mathematial transformation from curved to flat surface

Projected CRSs

There are many, many, many projected CRSs

All introduce distortion, eg in shape, area, distance, direction

No best one for all purposes

Selection depends on location, extent and purpose

Different Projected CRSs

Spatial Data

Spatial data is a more generic term that is not just for geographic data.

Spatial data are powerful because

dynamically determine spatial metrics like area and length,
characteristics like distance and direction, and
relationships like inside and intersects from these data.

Types of Spatial Data

Vector Data

Points, lines and Polygons

Raster Data

Regular grid of cells (or pixels)

We won’t be covering Raster data in this workshop*

Softare for working with Geospatial Data

Why special software?

Software for working with Geospatial Data

Most software can only query and analyze numbers and text

Databases, spreadsheets, statistical software packages

GIS - Geographic Information Systems

Software that can import, create, store, edit, visualize and analyze geospatial data

data types/objects to represent geospatial data
- locations represented with coordinates
- and referenced to the surface of the earth via CRSs
methods to operate on those representations

Types of GIS

Desktop GIS - ArcGIS, QGIS

Software extended to support geospatial data - Tableau, R

Spatial Databases - Postgresql

Web GIS - ArcGIS Online, CARTO

Custom software - leaflet web maps

Why R for Geospatial Data?

You already use R

Reproducibility

Free & Open Source

Cutting edge

Thousands of Cool add-ons

Shiny, rleaflet

Geospatial Data File formats

Common File Formats

Vector points, lines & polygons:

CSV
ESRI Shapefile
GeoJSON
KML

Raster grids

TIFF, GeoTiff
JPEG, JPEG2000
ASCII Grid

See GIS file formats

ESRI Shapefile

This is one of the most, if not the most common spatial vector data file formats.

Old but everywhere!

Gotchas: 2GB limit, 10 char column names, poor unicode support

CSV (Comma Separated Values) Files

The simplest, most common format for point data

"ID","name”,”x”,”y”,”taste","price","crowded","food"
"1","babette",-122.255374,37.868428,10,4,1,1
"2","musical",-122.260698,37.868383,7,3.25,1,1
"3","starbucks",-122.266057,37.870441,6,2.95,1,0
"4","yalis",-122.266385,37.873528,7,2.95,0,0
"5","berkeleyesp",-122.268681,37.873664,3,3.25,1,0
"6","fertile",-122.268863,37.874934,5,3.25,0,1

Geospatial Data in R

There are many approaches to and packages for working with geospatial data in R.

One approach is to keep it simple and store geospatial data in a data frame.

This approach is most common when the data are point data in CSV files.

About the Sample Data

San Francisco Open Data Portal https://data.sfgov.org

SF Property Tax Rolls

This data set includes the Office of the Assessor-Recorder’s secured property tax roll spanning from 2007 to 2016.

We are using this as a proxy for home values.

Load the CSV file into a data frame

sfhomes <- read.csv('data/sf_properties_25ksample.csv')  
head(sfhomes,6)

Explore the data

class(sfhomes) # what type of data object?
dim(sfhomes) # how many rows and columns
str(sfhomes) # display the structure of the object
head(sfhomes) # take a look at the first 10 records
summary(sfhomes) # explore the range of values
hist(sfhomes$totvalue)  # plot he range of values for the totvalue column

Plot of points

plot(sfhomes$lon, sfhomes$lat) # using base plot function

Nice Maps with `ggplot2`

library(ggplot2)

ggplot() + geom_point(data=sfhomes, aes(lon,lat), col="red", size=1)

Color points by `totvalue`

ggplot() + geom_point(data=sfhomes, aes(lon,lat, col=totvalue))

Subset the Data

for SalesYear equal to 2015

sfhomes15 <- subset(sfhomes, as.numeric(SalesYear) == 2015)

nrow(sfhomes15) # How many records?

[1] 5849

Always Explore

hist(sfhomes15$totvalue) # What is the distribution of totvalue?

Challenge - Make a map

of sfhomes15 using ggplot and set the color to the totvalue column.

ggplot() + geom_point(data=sfhomes15, aes(x=lon, y=lat, col=totvalue))

`ggmap` extends `ggplot`

Create basemaps on which you can display your data.

Geocode place names and addresses to get point coordinates.

and more…

ggmap

Load the libary

library(ggmap)

Google Maps API Terms of Service: http://developers.google.com/maps/terms.

Please cite ggmap if you use it: see citation("ggmap") for details.

ggmap

Some functionality may require you to register a Google API key See this StackOverflow discussion

#devtools::install_github("dkahle/ggmap")
#library(ggmap)
#register_google(key="XXXXX") # your key here

get_map

Fetch map data (default=Google) to plot using get_map

Use ?get_map for details

sf_map <- get_map("San Francisco, CA")

Source : https://maps.googleapis.com/maps/api/staticmap?center=San+Francisco,+CA&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyDHsYNZmqmTNvFn36tq470iRyXgQEU-PSE

Source : https://maps.googleapis.com/maps/api/geocode/json?address=San%20Francisco%2C%20CA&key=AIzaSyDHsYNZmqmTNvFn36tq470iRyXgQEU-PSE

This use of the command leverages the Google Geocoding API.

Display the ggmap of SF

ggmap(sf_map)

Syntax similar to `ggplot`

ggmap(sf_map) +
  geom_point(data=sfhomes15, aes(x=lon, y=lat, col=totvalue))

Customize `get_map`

Create a basemap zoomed to the extent of our data

# Get the center point of the data
sf_ctr <- c(lon = mean(sfhomes15$lon), lat = mean(sfhomes15$lat))
sf_ctr  # take a look

       lon        lat 
-122.43167   37.76017

# create the map
sf_basemap <- get_map(sf_ctr, zoom=12, scale=1)

Source : https://maps.googleapis.com/maps/api/staticmap?center=37.760172,-122.431673&zoom=12&size=640x640&scale=1&maptype=terrain&language=en-EN&key=AIzaSyDHsYNZmqmTNvFn36tq470iRyXgQEU-PSE

Plot the basemap with the data overlay

ggmap(sf_basemap) +
  geom_point(data=sfhomes15, aes(x=lon, y=lat, col=totvalue))

Change the basemap

See ?get_map to get available options

sf_basemap_lite <- get_map(sf_ctr, zoom=12, scale=1, 
                            maptype = "toner-lite", source="stamen")

Source : https://maps.googleapis.com/maps/api/staticmap?center=37.760172,-122.431673&zoom=12&size=640x640&scale=2&maptype=terrain&key=AIzaSyDHsYNZmqmTNvFn36tq470iRyXgQEU-PSE

Source : http://tile.stamen.com/toner-lite/12/653/1582.png

Source : http://tile.stamen.com/toner-lite/12/654/1582.png

Source : http://tile.stamen.com/toner-lite/12/655/1582.png

Source : http://tile.stamen.com/toner-lite/12/656/1582.png

Source : http://tile.stamen.com/toner-lite/12/653/1583.png

Source : http://tile.stamen.com/toner-lite/12/654/1583.png

Source : http://tile.stamen.com/toner-lite/12/655/1583.png

Source : http://tile.stamen.com/toner-lite/12/656/1583.png

Source : http://tile.stamen.com/toner-lite/12/653/1584.png

Source : http://tile.stamen.com/toner-lite/12/654/1584.png

Source : http://tile.stamen.com/toner-lite/12/655/1584.png

Source : http://tile.stamen.com/toner-lite/12/656/1584.png

Change the basemap

ggmap(sf_basemap_lite) +
  geom_point(data=sfhomes15, aes(x=lon, y=lat, col=totvalue))

Facets / Small Multiples / Micro Maps

# Let's look at last 5 years
sfhomes2010_15 <- subset(sfhomes, as.numeric(SalesYear) > 2009)

ggmap(sf_basemap_lite) +
  geom_point(aes(lon, lat, col=totvalue), data = sfhomes2010_15 )  +
  facet_wrap(~ SalesYear)

Facet/ Small Multiples / Micro Maps

ggmap(sf_basemap_lite) +
  geom_point(aes(lon, lat, col=totvalue), data = sfhomes2010_15 )  +
  facet_wrap(~ SalesYear)

Challenge

Redo above facet map with the following changes:

Use data from 1995 - 1999
Use a different basemap (eg maptype=“terrain”)

Challenge

sfhomes1995_1999 <- subset(sfhomes, (as.numeric(SalesYear) >= 1995) & (as.numeric(SalesYear) <= 1999))

ggmap(sf_basemap_lite) +
  geom_point(aes(lon, lat, col=totvalue), data = sfhomes1995_1999 )  +
  facet_wrap(~ SalesYear)

GGMap and GGPlot are great!

BUT!

There are limits to what you can do with geospatial data stored in a dataframe

and mapping the data with ggplot and ggmap

Can’t read & plot geospatial data files

Shapefile is the most common format for non-point geospatial data.

Can’t directly read or plot a shapefile with data frames/ggplot/ggmap

Can’t transform Coordinate Data

Spatial Analysis

What properties are within walking distance (.25 miles) of a BART station?
Average sales price by census tract?

Most spatial operations require spatial objects and methods

Spatial Data Objects in R

sp Package

The `sp` Package

Classes and Methods for Spatial Data

The SP package is most commonly used to construct and manipulate spatial data objects in R.

Hundreds of other R packages that do things with spatial data typically build on SP objects.

sp package

`sp` package

Take a look at the different types of spatial object classes supported by sp

library(sp)
getClass("Spatial")

Class "Spatial" [package "sp"]

Slots:
                              
Name:         bbox proj4string
Class:      matrix         CRS

Known Subclasses: 
Class "SpatialPoints", directly
Class "SpatialMultiPoints", directly
Class "SpatialGrid", directly
Class "SpatialLines", directly
Class "SpatialPolygons", directly
Class "SpatialPointsDataFrame", by class "SpatialPoints", distance 2
Class "SpatialPixels", by class "SpatialPoints", distance 2
Class "SpatialMultiPointsDataFrame", by class "SpatialMultiPoints", distance 2
Class "SpatialGridDataFrame", by class "SpatialGrid", distance 2
Class "SpatialLinesDataFrame", by class "SpatialLines", distance 2
Class "SpatialPixelsDataFrame", by class "SpatialPoints", distance 3
Class "SpatialPolygonsDataFrame", by class "SpatialPolygons", distance 2

```

sp Vector Objects

Geometry	Spatial Object	Spatial Object with Attributes
Points	SpatialPoints	SpatialPointsDataFrame
Lines	SpatialLines	SpatialLinesDataFrame
Polygons	SpatialPolygons	SpatialPolygonsDataFrame

We use the S*DF objects most frequently!

From Data Frame to SpatialPointsDataFrame

Let’s transform the sfhomes15 data frame to an sp object of type SpatialPointsDataFrame

`sp::coordinates()`

Use the sp::coordinates() method - Sets or retrieves spatial coordinates

When transforming a DF to SPDF, requires - the object that will get the coordinates - the names of the columns that contain the X and Y coordinates

Data Frame to SPDF

# First make a copy of the data frame
sfhomes15_sp <- sfhomes15

coordinates(sfhomes15_sp) <- c('lon','lat') # ORDER MATTERS!!

class(sfhomes15_sp) # check it

[1] "SpatialPointsDataFrame"
attr(,"package")
[1] "sp"

SpatialPointsDataFrame

sp::coordinates()

You transformed a data frame to an SPDF using:

coordinates(sfhomes15_sp) <- c('lon','lat')

Now try this:

coordinates(sfhomes15_sp)

Compare the SPDF to DF

str(sfhomes15) # the data frame

'data.frame':   5849 obs. of  19 variables:
 $ FiscalYear      : int  2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
 $ SalesDate       : Factor w/ 6639 levels "1920-01-30","1920-01-31",..: 6454 6542 6434 6424 6424 6420 6431 6428 6578 6578 ...
 $ Address         : Factor w/ 166180 levels "#200 0530-SANCHEZ             ST0000",..: 99415 108915 112360 112366 163280 72633 112370 142597 99359 99358 ...
 $ YearBuilt       : int  1992 1939 2014 2014 1900 NA 2014 NA 1992 1992 ...
 $ NumBedrooms     : int  0 0 1 1 0 0 2 0 0 0 ...
 $ NumBathrooms    : int  0 0 1 1 0 0 2 0 0 0 ...
 $ NumRooms        : int  0 5 5 5 10 0 6 0 0 0 ...
 $ NumStories      : int  0 1 1 1 2 0 1 0 0 0 ...
 $ NumUnits        : int  1 1 1 1 1 1 1 1 1 1 ...
 $ AreaSquareFeet  : int  535 1150 772 799 7944 0 780 0 410 385 ...
 $ ImprovementValue: int  66140 71351 21336 21925 44282 0 25607 0 91929 91929 ...
 $ LandValue       : int  37447 34682 86048 88423 71728 121830 103271 129952 48607 48607 ...
 $ Neighborhood    : Factor w/ 41 levels "","Bayview Hunters Point",..: 14 36 20 20 20 29 20 4 14 14 ...
 $ Location        : Factor w/ 124331 levels "(37.7081643340418, -122.451068309854)",..: 111624 68529 89779 89779 65168 16032 89779 86880 111624 111624 ...
 $ SupeDistrict    : int  5 4 9 9 9 9 9 8 5 5 ...
 $ totvalue        : int  103587 106033 107384 110348 116010 121830 128878 129952 140536 140536 ...
 $ SalesYear       : int  2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
 $ lat             : num  37.8 37.8 37.8 37.8 37.8 ...
 $ lon             : num  -122 -122 -122 -122 -122 ...

SPDF

str(sfhomes15_sp) # the SPDF

Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
  ..@ data       :'data.frame': 5849 obs. of  17 variables:
  .. ..$ FiscalYear      : int [1:5849] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
  .. ..$ SalesDate       : Factor w/ 6639 levels "1920-01-30","1920-01-31",..: 6454 6542 6434 6424 6424 6420 6431 6428 6578 6578 ...
  .. ..$ Address         : Factor w/ 166180 levels "#200 0530-SANCHEZ             ST0000",..: 99415 108915 112360 112366 163280 72633 112370 142597 99359 99358 ...
  .. ..$ YearBuilt       : int [1:5849] 1992 1939 2014 2014 1900 NA 2014 NA 1992 1992 ...
  .. ..$ NumBedrooms     : int [1:5849] 0 0 1 1 0 0 2 0 0 0 ...
  .. ..$ NumBathrooms    : int [1:5849] 0 0 1 1 0 0 2 0 0 0 ...
  .. ..$ NumRooms        : int [1:5849] 0 5 5 5 10 0 6 0 0 0 ...
  .. ..$ NumStories      : int [1:5849] 0 1 1 1 2 0 1 0 0 0 ...
  .. ..$ NumUnits        : int [1:5849] 1 1 1 1 1 1 1 1 1 1 ...
  .. ..$ AreaSquareFeet  : int [1:5849] 535 1150 772 799 7944 0 780 0 410 385 ...
  .. ..$ ImprovementValue: int [1:5849] 66140 71351 21336 21925 44282 0 25607 0 91929 91929 ...
  .. ..$ LandValue       : int [1:5849] 37447 34682 86048 88423 71728 121830 103271 129952 48607 48607 ...
  .. ..$ Neighborhood    : Factor w/ 41 levels "","Bayview Hunters Point",..: 14 36 20 20 20 29 20 4 14 14 ...
  .. ..$ Location        : Factor w/ 124331 levels "(37.7081643340418, -122.451068309854)",..: 111624 68529 89779 89779 65168 16032 89779 86880 111624 111624 ...
  .. ..$ SupeDistrict    : int [1:5849] 5 4 9 9 9 9 9 8 5 5 ...
  .. ..$ totvalue        : int [1:5849] 103587 106033 107384 110348 116010 121830 128878 129952 140536 140536 ...
  .. ..$ SalesYear       : int [1:5849] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
  ..@ coords.nrs : int [1:2] 19 18
  ..@ coords     : num [1:5849, 1:2] -122 -122 -122 -122 -122 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:5849] "770" "1345" "1603" "2300" ...
  .. .. ..$ : chr [1:2] "lon" "lat"
  ..@ bbox       : num [1:2, 1:2] -122.5 37.7 -122.4 37.8
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:2] "lon" "lat"
  .. .. ..$ : chr [1:2] "min" "max"
  ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
  .. .. ..@ projargs: chr NA

SPDF Objects

You can see from str(sfhomes) that a SPDF object is a collection of slots or components. The key ones are:

@data data frame of attributes that describe each location
@coords the coordinates for each geometric object - here points
@bbox the min and max bounding coordinates
@proj4string the coordinate reference system defintion as a string

Explore the object in the Environment window

SPDF Slots

Review the output of each of these:

summary(sfhomes15_sp)
head(sfhomes15_sp@data)
class(sfhomes15_sp@data)

sfhomes15_sp@bbox
bbox(sfhomes15_sp)

head(sfhomes15_sp@coords)
head(sfhomes15_sp$lat)
head(sfhomes15_sp$lon)

sfhomes15_sp@proj4string
proj4string(sfhomes15_sp)

Take a closer look

Look at sfhomes15_sp in the environment window

What’s missing

Are all the columns that were present in sfhomes15 also in sfhomes15_sp?

Is there a slot in sfhomes15_sp without data?

What is the CRS of the data?

proj4string(sfhomes15_sp) # get a CRS object

[1] NA

NA

Map an `SpatialPointsDataFrame`

plot(sfhomes15_sp)  # using sp::plot

So…..?

Recap

We created great maps of sfhomes point data with ggplot and ggmap.

Then we created a simple map of the SPDF with plot.

We aren’t seeing the value of sp objects just yet.

Let’s add more geospatial data

Reading in Geospatial Data

There’s an R package for that!

`rgdal`

rgdal is an R port of the powerful and widely used GDAL library.

It is the most commonly used R library for importing and exporting spatial data.

OGR: for vector data: readOGR() and writeOGR()
GDAL for raster data: readGDAL() and writeGDAL()

`rgdal`

library(rgdal)

Warning: package 'rgdal' was built under R version 3.4.3

rgdal: version: 1.2-16, (SVN revision 701)
 Geospatial Data Abstraction Library extensions to R successfully loaded
 Loaded GDAL runtime: GDAL 2.1.3, released 2017/20/01
 Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rgdal/gdal
 GDAL binary built with GEOS: FALSE 
 Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
 Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rgdal/proj
 Linking to sp version: 1.2-5

# See what file types are supported by rgdal drivers
# ogrDrivers()$name

Getting help

gdal.org

`?readOGR

For more info on working with rgdal to load different types of spatial data in R see this excellent tutorial by Zev Ross.

Read in a Shapefile with the boundary of San Francisco

Take a look at the file(s)

dir("data", pattern="sf_boundary")

[1] "sf_boundary.dbf" "sf_boundary.prj" "sf_boundary.shp" "sf_boundary.shx"

sf_boundary.dbf

sf_boundary.prj

sf_boundary.shp

sf_boundary.shx

Read in Shapefile

sfboundary <- readOGR(dsn="data",layer="sf_boundary")

OGR data source with driver: ESRI Shapefile 
Source: "data", layer: "sf_boundary"
with 1 features
It has 3 fields

# or
# sfboundary <- readOGR("data","sf_boundary")
# but not
#sfboundary <- readOGR(dsn="data/",layer="sf_boundary")

Check out the data structure

What type of sp object is sfboundary? How many features are in the object?

class(sfboundary)
str(sfboundary) 
head(sfboundary@data)

Explore the object in the Envi window

Make a quick plot of `sfboundary`

How?

Make a quick plot of `sfboundary`

plot(sfboundary)

Demonstration - A more complex `SpatialPolygonsDataFrame`

library(tigris)
calcounties <- counties(state="California", cb=T)
class(calcounties)
sf_cen <- subset(calcounties, COUNTYFP == "075")
plot(sf_cen)
head(sf_cen@data)
# Now look in Envi Window

What is the the sfboundary CRS

Is it a geographic or projected CRS?

proj4string(sfboundary)

Map both `sfboundary` & `sfhomes15_sp`

plot(sfboundary)
points(sfhomes15_sp, col="red")

Map both `sfboundary` & `sfhomes15_sp`

Where are the points? What’s wrong?

plot(sfboundary)
points(sfhomes15_sp, col="red")

What’s Wrong?

Compare the CRSs, are they the same?

proj4string(sfboundary)
proj4string(sfhomes15_sp)
proj4string(sfboundary) == proj4string(sfhomes15_sp)

Compare the CRSs, are they the same?

proj4string(sfboundary)

[1] "+proj=utm +zone=10 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"

+proj=utm +zone=10 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0

proj4string(sfhomes15_sp)

[1] NA

NA

proj4string(sfboundary) == proj4string(sfhomes15_sp)

[1] NA

Compare the coordinate values

sfboundary@bbox

        min       max
x  542696.6  556659.9
y 4173563.7 4185088.6

sfhomes15_sp@bbox

           min        max
lon -122.51072 -122.36964
lat   37.70821   37.80612

CRS Problems

The #1 reason…

CRS Definitions

All sp objects should have a defined CRS

If not, one needs to be assigned to the object - This is called defining a projection. - This doesn’t change the coordinates.

CRS Transformations

All sp objects should have the same CRS.

When they don’t, they need to be transformed to a common CRS.
This is also called a projection transformation,
- or projecting or reprojection.
Projection transformation returns a new spatial object with the transformed coordinates

CRS Definitions and Transformations

Geospatial-aware software will have a database of definitions for thousands of Earth referenced coordinate systems

Defining a CRS

We need to define the CRS of what sp object? - sfboundary or sfhomes15_sp?

We need to know - the appropriate CRS for the data - how to define the CRS - how to assign it to the sp object

Define a CRS

sp includes the CRS() function to define a CRS

Two ways

# use an EPSG code
proj4string(sfhomes15_sp) <- CRS("+init=epsg:4326") 

# or enter the proj4 string
# proj4string(sfhomes15_sp) <- CRS("+proj=longlat 
#                               +ellps=WGS84 +datum=WGS84 +no_defs")

check it

proj4string(sfhomes15_sp)

[1] "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0

Notes

proj4string() can get or set the CRS
proj4 is a library for managing map projections and CRSs
epsg codes are used as short-hand for CRSs definitions

Compare the CRSs, AGAIN

proj4string(sfboundary)

[1] "+proj=utm +zone=10 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"

+proj=utm +zone=10 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0

proj4string(sfhomes15_sp)

[1] "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0

proj4string(sfboundary) == proj4string(sfhomes15_sp)

[1] FALSE

Transforming a CRS

Transform the CRS

Use sp function spTransform

Requires as input:

a sp object to transform with a defined CRS
a target CRS

Outputs a new spatial object with coordinate data in the target CRS

It does not change the input data

Transform `sfboundary`

from UTM10 CRS to WGS84 (why?)

sfboundary_lonlat <- spTransform(sfboundary, CRS("+init=epsg:4326"))

# or
# sfboundary_lonlat <- spTransform(sfboundary, 
#                              CRS(proj4string(sfhomes15_sp)))

How do these two spTransform approaches differ?

Did it work?

How will we know?

Do the CRSs match?

proj4string(sfhomes15_sp) == proj4string(sfboundary_lonlat)

Overlay the data in space

plot(sfboundary_lonlat)
points(sfhomes15_sp, col="red")
points(sfhomes15_sp[sfhomes15_sp$totvalue<1000000,], col="green")

Overlay the data in space

Woo-hoo!

So….

We can transform sp objects to the same CRS for mapping and spatial analysis (we’ll get there!)

Save `sfboundary_lonlat`

Use writeOGR to save sfboundary_lonlat to a new shapefile

See ?writeOGR for help

# write transformed data to a new shapefile 
writeOGR(sfboundary_lonlat, 
          dsn = "data", 
          layer = "sfbounary_lonlat", 
          driver="ESRI Shapefile")

# is it there?
dir("data")

Projections, CRS, oh my!

We want all data in the same CRS

Which one is best?

Common CRS Codes

Geographic CRSs

4326 Geographic, WGS84 (default for lon/lat)
4269 Geographic, NAD83 (USA Fed agencies like Census)

Projected CRSs

5070 USA Contiguous Albers Equal Area Conic
3310 CA ALbers Equal Area
26910 UTM Zone 10, NAD83 (Northern Cal)
3857 Web Mercator (web maps)

Finding CRS Codes

See http://spatialreference.org/

Use this site to find EPSG codes and proj4 CRS strings

QUESTIONS?

Break…..

Mapping Spatial Objects

So far we have created maps with

base::plot, ggplot, ggmap for geospatial data in data frames

great for creating maps given these types of data

sp::plot for sp objects

meh, but great for a quick look at spatial data

There is also sp::spplot

Use it to create quick maps

Can be used to create great maps

BUT complex, non-intuitive syntax = long ugly code

See examples in sp Gallery: Plotting maps with sp

spplot

# map of the sfhomes data by totalvaue
spplot(sfhomes15_sp,"totvalue")

tmap

tmap stands for thematic map

Great maps with less code than the alternatives

Syntax should be familar to ggplot2 users, but simpler

Relatively easy to create interactive maps

tmap starting points

?tmap
vignette(“tmap-nutshell”)
vignette(“tmap-modes”)
tmap in a Nutshell

tmap

Load the library

library(tmap)

Warning: package 'tmap' was built under R version 3.4.3

Quick tmap (qtm)

qtm(sfhomes15_sp)

tm_symbols

#FFFFFF00

blank

#666666

#666666

#000000

#666666

#000000

#666666

to

Less

than

or

more

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

#000000

#000000

#CCCCCC

#000000

left

bottom

right

bottom

left

bottom

bg.color

aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

sfhomes15_sp

metric

segment

midpoint

none

metric

segment

midpoint

long@lat38

km

Quick Interactive tmap

tmap_mode("view")

tmap mode set to interactive viewing

qtm(sfhomes15_sp)

Reset the mode to static plot

tmap_mode("plot")

tmap mode set to plotting

Challenge

Create a static qtm of sfboundary_lonlat

bonus, set the border color to black and the fill to beige

See ?qtm for description of all options

sfboundary_lonlat quickmap

qtm(sfboundary_lonlat, borders="black", fill="beige")

tm_fill

#000000

solid

#F5F5DC

to

Less

than

or

more

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

#000000

#000000

#CCCCCC

#000000

left

bottom

right

bottom

left

bottom

bg.color

aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

sfboundary_lonlat

metric

segment

midpoint

none

metric

segment

midpoint

long@lat38

km

Crafting complex tmaps

`tmap` Shapes and Graphic Elements

tmap’s flexibility comes in how it intitively allows you to layer spatial data and style the layers by data attributes

Use tm_shape(<sp_object>) to specifiy a geospatial data layer

Add + tm_<element>(...) to style the layer by data values

…and other options for creating a publication ready map

Exploring `tmap` functionality

?tmap_shape

?tmap_element

?tm_polygons (tm_fill, tm_borders)
?tm_symbols (tm_dots, etc…)
tmap in a Nutshell

“Slow” tmaps

tm_shape(sfboundary_lonlat) + tm_polygons(col="beige", border.col="black")

tm_fill

#000000

solid

#F5F5DC

to

Less

than

or

more

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

#000000

#000000

#CCCCCC

#000000

left

bottom

right

bottom

left

bottom

bg.color

aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

sfboundary_lonlat

metric

segment

midpoint

none

metric

segment

midpoint

long@lat38

km

`tmap` of totvalue

tm_shape(sfhomes15_sp) + tm_dots(col="totvalue", size=.5)

totvalue

tm_symbols

#FFFFFF00

blank

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#666666

0 to 500,000

500,000 to 1,000,000

1,000,000 to 1,500,000

1,500,000 to 2,000,000

2,000,000 to 2,500,000

2,500,000 to 3,000,000

#FFF8C4

#FEE494

#FEC24D

#FB9225

#E3650E

#B74202

#666666

#FB9225

#666666

#FB9225

#666666

totvalue

totvalue

to

Less

than

or

more

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

#000000

#000000

#CCCCCC

#000000

left

bottom

right

bottom

left

bottom

bg.color

aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

sfhomes15_sp

metric

segment

midpoint

none

metric

segment

midpoint

long@lat38

km

Challenge

Make the previous map interactive - NOTE: columnn names must be quoted!

Zoom in on light & dark colored points to see if the corressponding totvalue makes sense.

Reset the mode to static

tmap_mode("plot")

tmap mode set to plotting

Overlaying layers

tm_shape(sfboundary_lonlat) + tm_polygons(col="black", border.col="grey") + 
   tm_shape(sfhomes15_sp) + tm_dots(col="totvalue", size=.5)

tm_fill

#BEBEBE

solid

#000000

to

Less

than

or

more

totvalue

tm_symbols

#FFFFFF00

blank

#FFF8C4

#FFF8C4

#FFF8C4

#FFF8C4

#FFF8C4

#FFF8C4

#FFF8C4

#FFF8C4

#FFF8C4

#FFF8C4

#FFF8C4

#FFF8C4

#FFF8C4

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#B74202

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#666666

0 to 500,000

500,000 to 1,000,000

1,000,000 to 1,500,000

1,500,000 to 2,000,000

2,000,000 to 2,500,000

2,500,000 to 3,000,000

#FFF8C4

#FEE494

#FEC24D

#FB9225

#E3650E

#B74202

#666666

#FB9225

#666666

#FB9225

#666666

totvalue

totvalue

to

Less

than

or

more

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

#000000

#000000

#CCCCCC

#000000

left

bottom

right

bottom

left

bottom

bg.color

aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

sfboundary_lonlat

sfhomes15_sp

metric

segment

midpoint

none

metric

segment

midpoint

long@lat38

km

Challenge

Redo the last map but

change the legend title to “San Francisco Home Sales, 2015”

hint: title=

tmap and CRSs

We have been mapping data in WGS84 (lon/lat) CRS.

Not a great idea for static maps of larger areas as distortion becomes evident.

Let’s explore that distortion and how to address with tmap

US States

Let’s load and map data for US states.

Data are in the file data/us_states_pop.shp

Challenge

Use readOGR to load data/us_states_pop.shp into an sp object named us_states

Read in & plot the Data

us_states <- readOGR("./data", "us_states_pop")

OGR data source with driver: ESRI Shapefile 
Source: "./data", layer: "us_states_pop"
with 49 features
It has 4 fields

Take a quick look

qtm(us_states)

tm_fill

#666666

solid

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

#D9D9D9

to

Less

than

or

more

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

#000000

#000000

#CCCCCC

#000000

left

bottom

right

bottom

left

bottom

bg.color

aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

us_states

metric

segment

midpoint

none

metric

segment

midpoint

long@lat37

km

Questions

Review us_states with the sp commands we used earlier and / or explore in the Environment window.

What type of sp object is us_states
How many features does it contain?
How many attributes describe those features?
What is the CRS?

Customizing the display

tm_shape(us_states) + tm_polygons(col="grey", border.col = "white")

tm_fill

#FFFFFF

solid

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

to

Less

than

or

more

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

#000000

#000000

#CCCCCC

#000000

left

bottom

right

bottom

left

bottom

bg.color

aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

us_states

metric

segment

midpoint

none

metric

segment

midpoint

long@lat37

km

CRS and Shape

Notice anything odd about shape of USA?

tm_fill

#FFFFFF

solid

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

to

Less

than

or

more

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

#000000

#000000

#CCCCCC

#000000

left

bottom

right

bottom

left

bottom

bg.color

aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

us_states

metric

segment

midpoint

none

metric

segment

midpoint

long@lat37

km

Dynamically Transforming the CRS

tm_shape(us_states, projection="+init=epsg:5070") + tm_polygons(col="grey", border.col = "white")

tm_fill

#FFFFFF

solid

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

to

Less

than

or

more

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

#000000

#000000

#CCCCCC

#000000

left

bottom

right

bottom

left

bottom

bg.color

aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

us_states

metric

segment

midpoint

none

metric

segment

midpoint

km

What’s happening here?

tm_shape(us_states, projection="+init=epsg:5070") + tm_polygons(col="grey", border.col = "white") +
tm_shape(us_states) + tm_borders(col="purple")

tm_fill

#FFFFFF

solid

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

#BEBEBE

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#BEBEBE

#BEBEBE

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to

Less

than

or

more

tm_fill

#A020F0

solid

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

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#000000

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aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

us_states

us_states

metric

segment

midpoint

none

metric

segment

midpoint

km

Dynamic CRS Transformations

Also called On-the-fly reprojection in ArcGIS & QGIS

Very cool!

BUT, if you want to use data in a different CRS it is best to transform it.

Challenge

Transform the us_states data to the USA Contiguous Albers CRS (5070),
Save output as a new SpatialPolygonsDataFrame called us_states_5070

us_states_5070

us_states_5070 <- spTransform(us_states, CRS("+init=epsg:5070"))

Plotting the Transformed Data

tm_shape(us_states_5070) + tm_polygons(col="beige") +
  tm_shape(us_states) + tm_borders(col="purple")

tm_fill

#666666

solid

#F5F5DC

#F5F5DC

#F5F5DC

#F5F5DC

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to

Less

than

or

more

tm_fill

#A020F0

solid

#FFFFFF

YlOrBr

RdYlGn

Set3

black

#000000

plain

#000000

WGS84

#FFFFFF

right

vertical

left

bottom

to

Less

than

or

more

#000000

left

top

#000000

#000000

#CCCCCC

#000000

left

bottom

right

bottom

left

bottom

bg.color

aes.color

aes.palette

attr.color

saturation

sepia.intensity

fontfamily

frame.double.line

compass.type

space.color

grey85

grey40

grey60

black

red

black

grey75

CartoDB.Positron

OpenStreetMap

Esri.WorldTopoMap

left

top

topright

Missing

us_states_5070

us_states

metric

segment

midpoint

none

metric

segment

midpoint

km

Questions?

Recap

Geospatial Data in R
CSV > Data Frame > ggplot/ggmap
GDAL, readOGR, writeOGR
Data Frame to SDPF
CRS definitions and transformations
Overlays
tmap

End of Part I

IMPORTANT ANNOUNCEMENT

`sf` package

The sf, or simple features package in R has many improvements

Based on open standards for specifying spatial data

ggplot and ggmap can map sf spatial objects

But most spatial packages still depend on sp

So, live on the bleeding edge or check back in a year or so.

Output code to script

library(knitr)
purl("r-geospatial-workshop-sp2018-pt1.Rmd", output = "scripts/r-geospatial-workshop-sp2018-pt1.r", documentation = 1)

---
title: "Geospatial Data in R, pt1"
author: "Patty Frontiera"
date: "Spring 2018"
output:  html_notebook #ioslides_presentation
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T)
```

## PREP

1. https://github.com/dlab-geo/r-geospatial-workshop

- Click *Clone or Download* and download the zip file
- Upzip the zip file and make a note of the folder in which it is located

2. Open RStudio and start a new script

3. Follow along by opening `r-geospatial-workshop-pt1-sp2018.html`

4. Install required libraries

```{r, eval=F}
install.packages(
  c("sp","rgdal","tmap","classInt","RColorBrewer",
    "ggplot2","leaflet", "ggmap"), dependencies=TRUE
)
```

# Geospatial Data in R

## Workshop Goals

* Intro to geospatial data 

* Intro to coordinate reference systems (CRS)

* Classes & methods for working with spatial data in R

* Mapping geospatial data

* Practice


## About Me

Patty Frontiera

[dlab.berkeley.edu](http://dlab.berkeley.edu)

## About you

Who are you?

Why are you here?


## Getting Started

1. Get the workshop files

https://github.com/dlab-geo/r-geospatial-workshop

- Click *Clone or Download* and download the zip file

- Upzip the zip file and make a note of the folder in which it is located

## Getting Started

2. Open RStudio and start a new script

3. Follow along by opening `r-geospatial-workshop-pt1-sp2018.html`

## Getting Started

4. In RStudio, install required libraries

```{r, eval=F}

install.packages(
  c("sp","rgdal","tmap","classInt","RColorBrewer",
    "ggplot2","leaflet", "ggmap"), dependencies=TRUE
)

```


# Geographic Data


## Geographic Data

are data about *locations* on or near the surface of the *Earth*.

<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/ee/Anatone_WA.jpg/640px-Anatone_WA.jpg"></img>


## Place names

convey geographic information but don't **unambigously** specify location

<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/ee/Anatone_WA.jpg/640px-Anatone_WA.jpg"></img>


## Geospatial data

represent location geometrically with **coordinates**

`46.130479, -117.134167`
  
<img width="600px" src="images/anatone_google.png"></img>


## Coordinate Reference System

Coordinates indicate specific locations on the Earth when associated with a geographic `coordinate reference system` or **CRS**. 

<img width="600px" src="https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/Latitude_and_Longitude_of_the_Earth.svg/640px-Latitude_and_Longitude_of_the_Earth.svg.png"></img>

## Geographic Coordinate Reference Systems

specify 

1. the shape of the Earth as the major & minor axes of an ellipsoid
2. the origin (0,0) - equator (~X), prime meridian (~Y)
3. fit of the CRS to the Earth (center of the earth), aka Datum
4. units, eg lat/lon expressed as decimal degrees or DMS

Because of variations in 1-3, there are **many** geographic CRSs!

## WGS84

The [World Geodetic System of 1984](https://en.wikipedia.org/wiki/World_Geodetic_System) is the default geographic CRS used today.

`WGS84` is the default CRS for most GIS software

Almost all data with lon/lat coordinates are assumed to be WGS84 unless otherwise specified

[NAD83] is another common geographic CRS used by US agencies like the Census.

`WGS84` and `NAD83` are so similar that differences are often ignored except for applications requiring high locational accuracy.


## Map Projections

A `Projected CRS` applies a **map projection** to a Geographic CRS

`Map Projection`: mathematial transformation from curved to flat surface

<img width="600px" src="images/projection.gif"></img>


## Projected CRSs

There are many, many, many projected CRSs

All introduce distortion, eg in shape, area, distance, direction

No best one for all purposes

Selection depends on location, extent and purpose

##  Different Projected CRSs

<img height="500px" src="images/diff_crs.png"></img>


## Spatial Data

Spatial data is a more generic term that is not just for geographic data. 

## Spatial data are powerful because

- dynamically determine spatial metrics like area and length, 
- characteristics like distance and direction, and 
- relationships like inside and intersects from these data.

# Types of Spatial Data


## Vector Data

Points, lines and Polygons

<img width="800px" src="images/vector_data.png"></img>

## Raster Data

Regular grid of cells (or pixels)

<img width="800px" src="images/raster_data.png"></img>

* We won't be covering Raster data in this workshop*

## Softare for working with Geospatial Data

- Why special software?

## Software for working with Geospatial Data

Most software can only query and analyze numbers and text

- Databases, spreadsheets, statistical software packages

## GIS - Geographic Information Systems

Software that can import, create, store, edit, visualize and analyze geospatial data

- data types/objects to represent geospatial data
    - locations represented with coordinates 
    - and referenced to the surface of the earth via CRSs
    
- methods to operate on those representations


## Types of GIS

Desktop GIS - ArcGIS, QGIS

Software extended to support geospatial data - Tableau, R

Spatial Databases - Postgresql

Web GIS - ArcGIS Online, CARTO

Custom software - leaflet web maps

# Why R for Geospatial Data?

## Why R for Geospatial Data?

You already use R

Reproducibility 

Free & Open Source

Cutting edge 

Thousands of Cool add-ons

- Shiny, rleaflet


# Geospatial Data File formats

## Common File Formats

Vector points, lines & polygons:

* CSV
* [ESRI Shapefile](https://en.wikipedia.org/wiki/Shapefile)
* GeoJSON
* KML

Raster grids

* TIFF, GeoTiff
* JPEG, JPEG2000
* ASCII Grid

See [GIS file formats](https://en.wikipedia.org/wiki/GIS_file_formats)

## ESRI Shapefile

This is one of the most, if not the most common spatial vector data file formats.

<img src="images/shapefile.png"></img>

Old but everywhere!

Gotchas: 2GB limit, 10 char column names, poor unicode support


## CSV (Comma Separated Values) Files

The simplest, most common format for point data

<pre>
"ID","name”,”x”,”y”,”taste","price","crowded","food"
"1","babette",-122.255374,37.868428,10,4,1,1
"2","musical",-122.260698,37.868383,7,3.25,1,1
"3","starbucks",-122.266057,37.870441,6,2.95,1,0
"4","yalis",-122.266385,37.873528,7,2.95,0,0
"5","berkeleyesp",-122.268681,37.873664,3,3.25,1,0
"6","fertile",-122.268863,37.874934,5,3.25,0,1
</pre>

# Geospatial Data in R

## Geospatial Data in R

There are many approaches to and packages for working with geospatial data in R.

One approach is to keep it simple and store geospatial data in a data frame.

This approach is most common when the data are point data in CSV files.

## About the Sample Data

**San Francisco Open Data Portal**
[https://data.sfgov.org](https://data.sfgov.org)

[SF Property Tax Rolls](https://data.sfgov.org/Housing-and-Buildings/Assessor-Historical-Secured-Property-Tax-Rolls/wv5m-vpq2)

This data set includes the Office of the Assessor-Recorder’s secured property tax roll spanning from 2007 to 2016.

We are using this as a proxy for home values.

## Load the CSV file into a data frame
```{r, echo=F }
sfhomes <- read.csv('data/sf_properties_25ksample.csv')
```

```{r, eval=F }
sfhomes <- read.csv('data/sf_properties_25ksample.csv')  
head(sfhomes,6)

```

## Explore the data

```{r, eval=F}
class(sfhomes) # what type of data object?
dim(sfhomes) # how many rows and columns
str(sfhomes) # display the structure of the object
head(sfhomes) # take a look at the first 10 records
summary(sfhomes) # explore the range of values
hist(sfhomes$totvalue)  # plot he range of values for the totvalue column
```

## Plot of points

```{r}
plot(sfhomes$lon, sfhomes$lat) # using base plot function
```

## Nice Maps with `ggplot2`

```{r}
library(ggplot2)

ggplot() + geom_point(data=sfhomes, aes(lon,lat), col="red", size=1)
```
 


## Color points by `totvalue`
```{r}
ggplot() + geom_point(data=sfhomes, aes(lon,lat, col=totvalue))
```


## Subset the Data

for `SalesYear` equal to 2015

```{r}
sfhomes15 <- subset(sfhomes, as.numeric(SalesYear) == 2015)

nrow(sfhomes15) # How many records?

```

## Always Explore
```{r}
hist(sfhomes15$totvalue) # What is the distribution of totvalue?
```

## Challenge - Make a map

of `sfhomes15` using `ggplot` and set the color to the `totvalue` column.

## 
```{r}
ggplot() + geom_point(data=sfhomes15, aes(x=lon, y=lat, col=totvalue))
```


## `ggmap` extends `ggplot`

Create basemaps on which you can display your data.

Geocode place names and addresses to get point coordinates.

and more...

## ggmap

Load the libary
```{r}
library(ggmap)
```

## ggmap

Some functionality may require you to register a Google API key
See this [StackOverflow discussion](https://stackoverflow.com/questions/34402979/increase-the-api-limit-in-ggmaps-geocode-function-in-r)

```{r}
#devtools::install_github("dkahle/ggmap")
#library(ggmap)
#register_google(key="XXXXX") # your key here
```
```{r, echo=FALSE}
#devtools::install_github("dkahle/ggmap")
#library(ggmap)
#register_google(key="AIzXXXXXXXXXXXXXXXXXXXXXxPSE") # your key here
register_google(key="AIzaSyDHsYNZmqmTNvFn36tq470iRyXgQEU-PSE")
```

##  get_map

Fetch map data (default=Google) to plot using `get_map`

Use `?get_map` for details
```{r}
sf_map <- get_map("San Francisco, CA")  
```

This use of the command leverages the Google Geocoding API.


## Display the ggmap of SF

```{r}
ggmap(sf_map)
```

## Syntax similar to `ggplot`

```{r}
ggmap(sf_map) +
  geom_point(data=sfhomes15, aes(x=lon, y=lat, col=totvalue))

```

## Customize `get_map`

Create a basemap zoomed to the extent of our data
```{r}

# Get the center point of the data
sf_ctr <- c(lon = mean(sfhomes15$lon), lat = mean(sfhomes15$lat))
sf_ctr  # take a look

# create the map
sf_basemap <- get_map(sf_ctr, zoom=12, scale=1)
```

## Plot the basemap with the data overlay
```{r}
ggmap(sf_basemap) +
  geom_point(data=sfhomes15, aes(x=lon, y=lat, col=totvalue))
```

## Change the basemap

See `?get_map` to get available options
```{r}
sf_basemap_lite <- get_map(sf_ctr, zoom=12, scale=1, 
                            maptype = "toner-lite", source="stamen")
```

## Change the basemap

```{r}
ggmap(sf_basemap_lite) +
  geom_point(data=sfhomes15, aes(x=lon, y=lat, col=totvalue))

```


## Facets / Small Multiples / Micro Maps
```{r, eval=F}
# Let's look at last 5 years
sfhomes2010_15 <- subset(sfhomes, as.numeric(SalesYear) > 2009)

ggmap(sf_basemap_lite) +
  geom_point(aes(lon, lat, col=totvalue), data = sfhomes2010_15 )  +
  facet_wrap(~ SalesYear)

```

```{r, echo=F}
# DO DONT SHOW
# Let's look at last 5 years
sfhomes2010_15 <- subset(sfhomes, as.numeric(SalesYear) > 2005)
```

## Facet/ Small Multiples / Micro Maps
```{r}
ggmap(sf_basemap_lite) +
  geom_point(aes(lon, lat, col=totvalue), data = sfhomes2010_15 )  +
  facet_wrap(~ SalesYear)

```

## Challenge

Redo above facet map with the following changes:

- Use data from 1995 - 1999
- Use a different basemap (eg maptype="terrain")

## Challenge
```{r}
sfhomes1995_1999 <- subset(sfhomes, (as.numeric(SalesYear) >= 1995) & (as.numeric(SalesYear) <= 1999))

ggmap(sf_basemap_lite) +
  geom_point(aes(lon, lat, col=totvalue), data = sfhomes1995_1999 )  +
  facet_wrap(~ SalesYear)
```

# GGMap and GGPlot are great!

## BUT!

There are limits to what you can do with geospatial data stored in a dataframe

and mapping the data with `ggplot` and `ggmap`

## Can't read & plot geospatial data files

Shapefile is the most common format for non-point geospatial data.

Can't directly read or plot a shapefile with data frames/ggplot/ggmap

<img src="images/shapefile.png" width="400px"></img>

## Can't transform Coordinate Data

<img src="images/bart_sf_df.png" width="600px"><img>


## Spatial Analysis

- What properties are within walking distance (.25 miles) of a BART station?

- Average sales price by census tract?

Most spatial operations require spatial objects and methods


# Spatial Data Objects in R


# sp Package

## The `sp` Package

**Classes and Methods for Spatial Data**

The `SP` package is most commonly used to construct and manipulate spatial data objects in R. 

**Hundreds** of other R packages that do things with spatial data typically build on SP objects.


# sp package

## `sp` package

Take a look at the different types of spatial object classes supported by `sp`

```{r}
library(sp)
getClass("Spatial") 
```

```

## sp Vector Objects
 Geometry | Spatial Object | Spatial Object with Attributes 
 ---------| --------------- | ------------------------------
 Points | SpatialPoints | SpatialPointsDataFrame 
 Lines | SpatialLines | SpatialLinesDataFrame 
 Polygons | SpatialPolygons | SpatialPolygonsDataFrame
 
<pre>

</pre>

We use the S*DF objects most frequently!


# From Data Frame to SpatialPointsDataFrame

## From Data Frame to SpatialPointsDataFrame

Let's transform the `sfhomes15` data frame to an `sp` object of type `SpatialPointsDataFrame`


## `sp::coordinates()` 

Use the `sp::coordinates()` method
- Sets or retrieves spatial coordinates

When transforming a DF to SPDF, requires 
- the object that will get the coordinates
- the names of the columns that contain the X and Y coordinates


## Data Frame to SPDF

```{r}

# First make a copy of the data frame
sfhomes15_sp <- sfhomes15

coordinates(sfhomes15_sp) <- c('lon','lat') # ORDER MATTERS!!

class(sfhomes15_sp) # check it
```

## sp::coordinates()

You transformed a data frame to an SPDF using:
```
coordinates(sfhomes15_sp) <- c('lon','lat')
```

Now try this:
```
coordinates(sfhomes15_sp)
```

## Compare the SPDF to DF
```{r}
str(sfhomes15) # the data frame
```

## SPDF
```{r}
str(sfhomes15_sp) # the SPDF
```
 

# SPDF Objects

## SPDF Objects

You can see from **str(sfhomes)** that a SPDF object is a collection of slots or components. The key ones are:

- `@data` data frame of attributes that describe each location
- `@coords` the coordinates for each geometric object - here points 
- `@bbox` the min and max bounding coordinates
- `@proj4string` the coordinate reference system defintion as a string

*Explore the object in the Environment window*

## SPDF Slots
 
Review the output of each of these:

```{r, eval=F}

summary(sfhomes15_sp)
head(sfhomes15_sp@data)
class(sfhomes15_sp@data)

sfhomes15_sp@bbox
bbox(sfhomes15_sp)

head(sfhomes15_sp@coords)
head(sfhomes15_sp$lat)
head(sfhomes15_sp$lon)

sfhomes15_sp@proj4string
proj4string(sfhomes15_sp)

```

## Take a closer look

Look at `sfhomes15_sp` in the environment window


## What's missing

Are all the columns that were present in `sfhomes15` also in `sfhomes15_sp`?

Is there a slot in `sfhomes15_sp` without data?


## What is the CRS of the data?
```{r}

proj4string(sfhomes15_sp) # get a CRS object

```

## Map an `SpatialPointsDataFrame`
```{r}
plot(sfhomes15_sp)  # using sp::plot
```

# So.....?

## Recap

We created great maps of sfhomes point data with `ggplot` and `ggmap`.

Then we created a simple map of the `SPDF` with `plot`.

We aren't seeing the value of `sp` objects just yet.

# Let's add more geospatial data

## Reading in Geospatial Data

There's an R package for that! 

## `rgdal`

`rgdal` is an R port of the powerful and widely used [GDAL](http://gdal.org) library.

It is the most commonly used R library for importing and exporting spatial data. 

* `OGR`: for vector data: readOGR() and writeOGR()

* `GDAL` for raster data: readGDAL() and writeGDAL()

## `rgdal`

```{r}

library(rgdal)

# See what file types are supported by rgdal drivers
# ogrDrivers()$name

```

## Getting help

gdal.org

`?readOGR

For more info on working with `rgdal` to load different types of spatial data in R see this excellent [tutorial](http://zevross.com/blog/2016/01/13/tips-for-reading-spatial-files-into-r-with-rgdal/) by Zev Ross.

## Read in a Shapefile with the boundary of San Francisco

Take a look at the file(s)
```{r}

dir("data", pattern="sf_boundary")

```

## Read in Shapefile

```{r}

sfboundary <- readOGR(dsn="data",layer="sf_boundary")

# or
# sfboundary <- readOGR("data","sf_boundary")
# but not
#sfboundary <- readOGR(dsn="data/",layer="sf_boundary")

```


## Check out the data structure

What type of `sp` object is `sfboundary`?
How many `features` are in the object?

```{r, eval=F}
class(sfboundary)
str(sfboundary) 
head(sfboundary@data)  
```
*Explore the object in the Envi window*

## Make a quick plot of `sfboundary`

How?

## Make a quick plot of `sfboundary`
```{r}

plot(sfboundary)

```


## Demonstration - A more complex `SpatialPolygonsDataFrame`

```{r, eval=F}
library(tigris)
calcounties <- counties(state="California", cb=T)
class(calcounties)
sf_cen <- subset(calcounties, COUNTYFP == "075")
plot(sf_cen)
head(sf_cen@data)
# Now look in Envi Window

```


## What is the the sfboundary CRS

Is it a geographic or projected CRS?

```{r, eval=F}

proj4string(sfboundary)

```

## Map both `sfboundary` & `sfhomes15_sp`
```{r}

plot(sfboundary)
points(sfhomes15_sp, col="red")

```

## Map both `sfboundary` & `sfhomes15_sp`

Where are the points? What's wrong?

```{r, eval=F}
plot(sfboundary)
points(sfhomes15_sp, col="red")
```

## What's Wrong?

Compare the CRSs, are they the same?

```{r, eval=F}

proj4string(sfboundary)
proj4string(sfhomes15_sp)
proj4string(sfboundary) == proj4string(sfhomes15_sp)

```

## Compare the CRSs, are they the same?

```{r}

proj4string(sfboundary)
proj4string(sfhomes15_sp)
proj4string(sfboundary) == proj4string(sfhomes15_sp)

```

## Compare the coordinate values

```{r}
sfboundary@bbox
sfhomes15_sp@bbox
```

## CRS Problems

The #1 reason...

<img width="600px" src="images/not_working.jpeg"></img>

## CRS Definitions

All `sp` objects should have a defined CRS

If not, one needs to be assigned to the object
- This is called `defining a projection`.
- This doesn't change the coordinates.

## CRS Transformations

All `sp` objects should have the same CRS.

- When they don't, they need to be transformed to a common CRS.

- This is also called a projection transformation, 
    - or `projecting` or `reprojection`.
    
- Projection transformation returns a new spatial object with the transformed coordinates



## CRS Definitions and Transformations

Geospatial-aware software will have a database of definitions for thousands of Earth referenced coordinate systems


## Defining a CRS

We need to define the CRS of what `sp` object?
- `sfboundary` or `sfhomes15_sp`?

We need to know 
- the appropriate CRS for the data
- how to define the CRS
- how to assign it to the `sp` object



## Define a CRS

`sp` includes the `CRS()` function to define a CRS

- Two ways

```{r}

# use an EPSG code
proj4string(sfhomes15_sp) <- CRS("+init=epsg:4326") 

# or enter the proj4 string
# proj4string(sfhomes15_sp) <- CRS("+proj=longlat 
#                               +ellps=WGS84 +datum=WGS84 +no_defs")  
```


## check it

```{r}
proj4string(sfhomes15_sp)
```

## Notes

- `proj4string()` can get or set the CRS
- `proj4` is a library for managing map projections and CRSs
- `epsg` codes are used as short-hand for CRSs definitions



## Compare the CRSs, AGAIN

```{r}

proj4string(sfboundary)
proj4string(sfhomes15_sp)
proj4string(sfboundary) == proj4string(sfhomes15_sp)

```


# Transforming a CRS

## Transform the CRS

Use `sp` function `spTransform`

Requires as input:

* a `sp` object to transform with a defined CRS
    
* a target CRS

Outputs a new spatial object with coordinate data in the target CRS

- It does not change the input data

## Transform `sfboundary`

from `UTM10 CRS` to `WGS84`  (why?)
```{r}

sfboundary_lonlat <- spTransform(sfboundary, CRS("+init=epsg:4326"))

# or
# sfboundary_lonlat <- spTransform(sfboundary, 
#                              CRS(proj4string(sfhomes15_sp)))

```

*How do these two `spTransform` approaches differ?*

## Did it work?

How will we know?

## Do the CRSs match?

```{r, eval=F}
proj4string(sfhomes15_sp) == proj4string(sfboundary_lonlat)

```

## Overlay the data in space

```{r, eval=F}
plot(sfboundary_lonlat)
points(sfhomes15_sp, col="red")
points(sfhomes15_sp[sfhomes15_sp$totvalue<1000000,], col="green")

```

## Overlay the data in space

Woo-hoo!

```{r, echo=F}
plot(sfboundary_lonlat)
points(sfhomes15_sp, col="red")
points(sfhomes15_sp[sfhomes15_sp$totvalue<1000000,], col="green")

```

## So....

We can transform `sp` objects to the same CRS for mapping and spatial analysis (we'll get there!)


## Save `sfboundary_lonlat`

Use `writeOGR` to save `sfboundary_lonlat` to a new shapefile

See `?writeOGR` for help
```{r, eval=F}
# write transformed data to a new shapefile 
writeOGR(sfboundary_lonlat, 
          dsn = "data", 
          layer = "sfbounary_lonlat", 
          driver="ESRI Shapefile")

# is it there?
dir("data")
```



## Projections, CRS, oh my!

We want all data in the same CRS

Which one is best?

<img height ="500px" src="https://d3enniz247y0a9.cloudfront.net/website/var/tmp/image-thumbnails/30000/34546/thumb__article-header-square/hero-square-gettyimages-563936217.jpeg"></img>


## Common CRS Codes

**Geographic CRSs**

* `4326` Geographic, WGS84 (default for lon/lat)

* `4269` Geographic, NAD83 (USA Fed agencies like Census)

**Projected CRSs**

* `5070` USA Contiguous Albers Equal Area Conic

* `3310` CA ALbers Equal Area

* `26910` UTM Zone 10, NAD83 (Northern Cal)

* `3857` Web Mercator (web maps)

## Finding CRS Codes

See [http://spatialreference.org/](http://spatialreference.org/)

Use this site to find EPSG codes and proj4 CRS strings

# QUESTIONS?

# Break.....


# Mapping Spatial Objects

## So far we have created maps with

`base::plot`, `ggplot`, `ggmap` for geospatial data in data frames

- great for creating maps given these types of data


`sp::plot` for `sp` objects

- meh, but great for a quick look at spatial data

## There is also sp::spplot

Use it to create quick maps

Can be used to create great maps 

- BUT complex, non-intuitive syntax = long ugly code

See examples in [sp Gallery: Plotting maps with sp](https://edzer.github.io/sp)

## spplot

```{r}
# map of the sfhomes data by totalvaue
spplot(sfhomes15_sp,"totvalue")
```

# tmap

## tmap

`tmap` stands for thematic map

Great maps with less code than the alternatives

Syntax should be familar to ggplot2 users, but simpler

Relatively easy to create interactive maps

## tmap starting points

- ?tmap
- vignette("tmap-nutshell")
- vignette("tmap-modes")
- [tmap in a Nutshell](https://cran.r-project.org/web/packages/tmap/vignettes/tmap-nutshell.html) 


## tmap

Load the library

```{r}
library(tmap)

```

## Quick tmap (qtm)

```{r}
qtm(sfhomes15_sp)
```

## Quick Interactive tmap

```{r}
tmap_mode("view")
qtm(sfhomes15_sp)
```

## Reset the mode to static plot

```{r}
tmap_mode("plot")
```


## Challenge

Create a static `qtm` of `sfboundary_lonlat`

- bonus, set the border color to black and the fill to beige

See `?qtm` for description of all options


## sfboundary_lonlat quickmap

```{r}
qtm(sfboundary_lonlat, borders="black", fill="beige")
```

# Crafting complex tmaps

## `tmap` Shapes and Graphic Elements

tmap's flexibility comes in how it intitively allows you to layer spatial data and style the layers by data attributes

Use `tm_shape(<sp_object>)`  to specifiy a geospatial data layer

Add `+ tm_<element>(...)` to style the layer by data values

...and other options for creating a publication ready map

## Exploring `tmap` functionality

?tmap_shape

?tmap_element

- ?tm_polygons (tm_fill, tm_borders)
- ?tm_symbols (tm_dots, etc...)

- [tmap in a Nutshell](https://cran.r-project.org/web/packages/tmap/vignettes/tmap-nutshell.html) 

## "Slow" tmaps
```{r}
tm_shape(sfboundary_lonlat) + tm_polygons(col="beige", border.col="black")
```

## `tmap` of totvalue

```{r}
tm_shape(sfhomes15_sp) + tm_dots(col="totvalue", size=.5)
```

## Challenge

Make the previous map interactive
- **NOTE**: columnn names must be quoted!

Zoom in on light & dark colored points to see if the corressponding `totvalue` makes sense.

## Reset the mode to static

```{r}
tmap_mode("plot")
```

## Overlaying layers

```{r}

tm_shape(sfboundary_lonlat) + tm_polygons(col="black", border.col="grey") + 
   tm_shape(sfhomes15_sp) + tm_dots(col="totvalue", size=.5)
```

## Challenge

Redo the last map but

change the legend title to "San Francisco Home Sales, 2015"

- hint: `title=`

# tmap and CRSs

## tmap and CRSs

We have been mapping data in WGS84 (lon/lat) CRS.

Not a great idea for static maps of larger areas as distortion becomes evident.

Let's explore that distortion and how to address with `tmap`

## US States

Let's load and map data for US states.

Data are in the file `data/us_states_pop.shp`


## Challenge

Use `readOGR` to load `data/us_states_pop.shp` into an `sp` object named `us_states`

## Read in & plot the Data
```{r}
us_states <- readOGR("./data", "us_states_pop")
```

## Take a quick look

```{r}
qtm(us_states)
```

## Questions

Review `us_states` with the `sp` commands we used earlier and / or explore in the Environment window.

- What type of `sp` object is `us_states`

- How many features does it contain?

- How many attributes describe those features?

- What is the CRS?


## Customizing the display

```{r}
tm_shape(us_states) + tm_polygons(col="grey", border.col = "white")
```

## CRS and Shape

Notice anything odd about shape of USA?
```{r, echo=F}
tm_shape(us_states) + tm_polygons(col="grey", border.col = "white")
```

## Dynamically Transforming the CRS

```{r}
tm_shape(us_states, projection="+init=epsg:5070") + tm_polygons(col="grey", border.col = "white")
```


## What's happening here?

```{r}
tm_shape(us_states, projection="+init=epsg:5070") + tm_polygons(col="grey", border.col = "white") +
tm_shape(us_states) + tm_borders(col="purple") 
```


## Dynamic CRS Transformations

Also called `On-the-fly reprojection` in ArcGIS & QGIS

Very cool!

BUT, if you want to use data in a different CRS it is best to transform it.

## Challenge

* Transform the `us_states` data to the USA Contiguous Albers CRS (5070),

* Save output as a new `SpatialPolygonsDataFrame` called `us_states_5070`

## us_states_5070
```{r}
us_states_5070 <- spTransform(us_states, CRS("+init=epsg:5070"))

```

## Plotting the Transformed Data

```{r}
tm_shape(us_states_5070) + tm_polygons(col="beige") +
  tm_shape(us_states) + tm_borders(col="purple")
```

# Questions?

## Recap

- Geospatial Data in R
- CSV > Data Frame > ggplot/ggmap
- GDAL, readOGR, writeOGR
- Data Frame to SDPF
- CRS definitions and transformations
- Overlays
- tmap

# End of Part I

# IMPORTANT ANNOUNCEMENT

## `sf` package

The `sf`, or `simple features` package in R has many improvements

Based on open standards for specifying spatial data

`ggplot` and `ggmap` can map `sf` spatial objects

But most spatial packages still depend on `sp`

So, live on the bleeding edge or check back in a year or so.


## Output code to script

```{r, eval=F}
library(knitr)
purl("r-geospatial-workshop-sp2018-pt1.Rmd", output = "scripts/r-geospatial-workshop-sp2018-pt1.r", documentation = 1)
```


Geospatial Data in R, pt1

Patty Frontiera

Spring 2018

PREP

Geospatial Data in R

Workshop Goals

About Me

About you

Getting Started

Getting Started

Getting Started

Geographic Data

Geographic Data

Place names

Geospatial data

Coordinate Reference System

Geographic Coordinate Reference Systems

WGS84

Map Projections

Projected CRSs

Different Projected CRSs

Spatial Data

Spatial data are powerful because

Types of Spatial Data

Vector Data

Raster Data

Softare for working with Geospatial Data

Software for working with Geospatial Data

GIS - Geographic Information Systems

Types of GIS

Why R for Geospatial Data?

Why R for Geospatial Data?

Geospatial Data File formats

Common File Formats

ESRI Shapefile

CSV (Comma Separated Values) Files

Geospatial Data in R